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Author : Albertus Jacob Es
Publisher :
ISBN 13 :
Total Pages : 15 pages
Book Rating : 4.:/5 (685 download)
Download or read book Simple Kernel Estimators for Certain Nonparametric Deconvolution Problems written by Albertus Jacob Es and published by . This book was released on 1997 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Alexander Meister
Publisher : Springer Science & Business Media
ISBN 13 : 3540875573
Total Pages : 211 pages
Book Rating : 4.5/5 (48 download)
Download or read book Deconvolution Problems in Nonparametric Statistics written by Alexander Meister and published by Springer Science & Business Media. This book was released on 2009-12-24 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deconvolution problems occur in many ?elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. During the last two decades, those topics have received more and more attention. As appli- tions of deconvolution procedures concern many real-life problems in eco- metrics, biometrics, medical statistics, image reconstruction, one can realize an increasing number of applied statisticians who are interested in nonpa- metric deconvolution methods; on the other hand, some deep results from Fourier analysis, functional analysis, and probability theory are required to understand the construction of deconvolution techniques and their properties so that deconvolution is also particularly challenging for mathematicians. Thegeneraldeconvolutionprobleminstatisticscanbedescribedasfollows: Our goal is estimating a function f while any empirical access is restricted to some quantity h = f?G = f(x?y)dG(y), (1. 1) that is, the convolution of f and some probability distribution G. Therefore, f can be estimated from some observations only indirectly. The strategy is ˆ estimating h ?rst; this means producing an empirical version h of h and, then, ˆ applying a deconvolution procedure to h to estimate f. In the mathematical context, we have to invert the convolution operator with G where some reg- ˆ ularization is required to guarantee that h is contained in the invertibility ˆ domain of the convolution operator. The estimator h has to be chosen with respect to the speci?c statistical experiment.
Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 18 pages
Book Rating : 4.:/5 (249 download)
Download or read book Multi Bandwidth Kernel Estimators for Nonparametric Deconvolution Problems written by A. J. van Es and published by . This book was released on 1999 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : M.P. Wand
Publisher : CRC Press
ISBN 13 : 9780412552700
Total Pages : 230 pages
Book Rating : 4.5/5 (527 download)
Download or read book Kernel Smoothing written by M.P. Wand and published by CRC Press. This book was released on 1994-12-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.
Author : Grace Y. Yi
Publisher : CRC Press
ISBN 13 : 1351588591
Total Pages : 648 pages
Book Rating : 4.3/5 (515 download)
Download or read book Handbook of Measurement Error Models written by Grace Y. Yi and published by CRC Press. This book was released on 2021-09-28 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research
Author : Robert David Murison
Publisher :
ISBN 13 :
Total Pages : 276 pages
Book Rating : 4.:/5 (222 download)
Download or read book Problems in Density Estimation for Independent and Dependent Data written by Robert David Murison and published by . This book was released on 1993 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Artur Gramacki
Publisher : Springer
ISBN 13 : 3319716883
Total Pages : 197 pages
Book Rating : 4.3/5 (197 download)
Download or read book Nonparametric Kernel Density Estimation and Its Computational Aspects written by Artur Gramacki and published by Springer. This book was released on 2017-12-21 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
Author : A. J. van Es
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.3/5 (91 download)
Download or read book Aspects of Nonparametric Density Estimation written by A. J. van Es and published by . This book was released on 1991 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 1244 pages
Book Rating : 4.3/5 (91 download)
Download or read book Mathematical Reviews written by and published by . This book was released on 1999 with total page 1244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Piet Groeneboom
Publisher : Cambridge University Press
ISBN 13 : 0521864011
Total Pages : 429 pages
Book Rating : 4.5/5 (218 download)
Download or read book Nonparametric Estimation under Shape Constraints written by Piet Groeneboom and published by Cambridge University Press. This book was released on 2014-12-11 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.
Author : Ming-Chung Liu
Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.:/5 (174 download)
Download or read book Nonparametric Density Function Estimation and the Deconvolution Problem written by Ming-Chung Liu and published by . This book was released on 1987 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : G.G Roussas
Publisher : Springer Science & Business Media
ISBN 13 : 9401132224
Total Pages : 691 pages
Book Rating : 4.4/5 (11 download)
Download or read book Nonparametric Functional Estimation and Related Topics written by G.G Roussas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.
Author : Aren J. H. van Es (Physiologist, Netherlands)
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (637 download)
Download or read book Convex Minorant Estimators of Distributions in Nonparametric Deconvolution Problems written by Aren J. H. van Es (Physiologist, Netherlands) and published by . This book was released on 1990 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sam Efromovich
Publisher : Springer Science & Business Media
ISBN 13 : 0387226389
Total Pages : 423 pages
Book Rating : 4.3/5 (872 download)
Download or read book Nonparametric Curve Estimation written by Sam Efromovich and published by Springer Science & Business Media. This book was released on 2008-01-19 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.
Author : Linruo Guo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
Download or read book Estimating Multivariate Density Function of Mixed Measurement Error Data written by Linruo Guo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Density estimation has been a long frontline research area in nonparametric smoothing. However, real applications oftentimes see the data contaminated with different types of measurement errors. Further data analysis, therefore, should take care of these errors to have a reliable statistical inference procedure. In this proposal, nonparametric density estimation for the data contaminated super-smooth, ordinary-smooth, Berkson measurement errors will be thoroughly investigated. Classical kernel and deconvolution kernel smoothing are used as building blocks to construct the estimators. In the first part, we propose a nonparametric mixed kernel estimator for a multivariate density function and its derivatives when the data are contaminated with different sources of measurement errors. The proposed estimator is a mixture of the classical and the deconvolution kernels, accounting for the error-free and error- prone variables, respectively. Large sample properties of the proposed nonparametric estimator, including the order of the mean squares error, the consistency, and the asymptotic normality, are discussed. The optimal convergence rates among all nonparametric estimators for different measurement error structures are derived, and it is shown that the proposed mixed kernel estimators achieve the optimal convergence rate. A simulation study is conducted to evaluate the finite sample performance of the proposed estimators. In the second part, we consider the nonparametric estimation for the joint density function of two random variables, when one variable is contaminated with Berkson measurement error, and another variable can be observed directly. Two estimators are proposed with or without applying the kernel smoothing for the data with Berkson measurement error. Mean squared errors are calculated for both estimators. Large sample properties, including weak consistencies, strong consistencies, uniform strong consistencies in probability, and asymptotic normality are derived. In addition, we develop a method for bandwidth selection in the kernel estimate of the probability density using the least squares cross-validation method. The performance of this method is further assessed by a simulation study.
Author : Evarist Giné
Publisher : Cambridge University Press
ISBN 13 : 1009022784
Total Pages : 706 pages
Book Rating : 4.0/5 (9 download)
Download or read book Mathematical Foundations of Infinite-Dimensional Statistical Models written by Evarist Giné and published by Cambridge University Press. This book was released on 2021-03-25 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
Author : Jose Luis Rojo-Alvarez
Publisher : John Wiley & Sons
ISBN 13 : 1118611799
Total Pages : 665 pages
Book Rating : 4.1/5 (186 download)
Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
